## Source: local data table [475,719 x 102]
## Call: `_DT1`[!`_DT2`, on = .(word)]
##
## FACILITY_CITY ACTIVI…¹ OWNER…² OWNER…³ FACIL…⁴ RECOR…⁵ PROGR…⁶ PROGR…⁷ PROGR…⁸
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
## 1 ALHAMBRA 2021/10… OW0269… SKATE … FA0280… PR0235… SKATES… ACTIVE 1634
## 2 ALHAMBRA 2018/05… OW0031… SANCHE… FA0006… PR0037… BUN N … ACTIVE 1638
## 3 ALHAMBRA 2018/05… OW0031… SANCHE… FA0006… PR0037… BUN N … ACTIVE 1638
## 4 ALHAMBRA 2021/07… OW0033… STARBU… FA0048… PR0005… STARBU… ACTIVE 1633
## 5 ALHAMBRA 2021/07… OW0033… STARBU… FA0048… PR0005… STARBU… ACTIVE 1633
## 6 ALHAMBRA 2019/05… OW0185… SAN TU… FA0179… PR0173… RICK'S… ACTIVE 1638
## # … with 475,713 more rows, 93 more variables: PE_DESCRIPTION <chr>,
## # FACILITY_ADDRESS <chr>, FACILITY_STATE <chr>, FACILITY_ZIP <int>,
## # SERVICE_CODE <int>, SERVICE_DESCRIPTION <chr>, SCORE <int>,
## # SERIAL_NUMBER <chr>, EMPLOYEE_ID <chr>, ObjectId.x <int>, Pop_Tot <int>,
## # Prop_18y <dbl>, Prop_64y <dbl>, Prop_65y_ <dbl>, Prop_Blk <dbl>,
## # Prop_Lat <dbl>, Prop_Whi <dbl>, Prop_Asi <dbl>, Prop_Ami <dbl>,
## # Prop_NHO <dbl>, Prop_FPL1 <dbl>, Prop_FPL2 <dbl>, Prop_forb <dbl>, …
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
Chain restaurant inspection scores with heat map of proportion with
diabetes
DMmap